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Accurate Uncertainties for Deep Learning Using Calibrated Regression.
international conference on machine learning, (2018): 2801-2809
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Abstract
Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate -- for example, a 90%...More
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